A partner selection model for collaborative product innovation from the viewpoint of knowledge collaboration

Abstract

Purpose Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration. Design/methodology/approach First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection. Findings A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance. Originality/value Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

Member Selection for the Collaborative New Product Innovation Teams Integrating Individual and Collaborative Attributions

“…Meanwhile, the member selection model is a 0-1 quadratic programming optimization model, similar to the difference maximization model of Kuo et al, which has proved that this problem is NP-hard [49]. Moreover, the member selection model proposed in this work comprehensively considered the individual knowledge competence, knowledge complementarity, and knowledge collaboration performance among candidates, which is more systematic and reasonable in the context of the CNPI than other member modes of selection or partner selection models [12,29,35]. Specifically, Zhang and Zhang's [12] member selection model considered the two goals of team members' personality and interpersonal relationships.…”

“…Jiang et al [27] have put forward a transformation method that can reduce the complexity of the member selection model and improve the solution efficiency of the member selection problem. In addition, Jian et al [29] have proposed a nondominated sorting genetic algorithm II to solve the multi-objective partner selection problem in the context of collaborative product innovation. However, the decision-making methods proposed in the above works are mainly used to solve the member selection model based on individual attribution or indicator; however, it is difficult to directly use them for solving the member selection problem based on the individual and collaborative attributions.…”

“…Specifically, Zhang and Zhang's [12] member selection model considered the two goals of team members' personality and interpersonal relationships. Jian et al [29] established an evaluation index model integrating knowledge matching degree and overall revenue of innovation alliance. e above models are difficult to deal with the complex requirements for the CNPI team member selection, while it is the advantage of the proposed member selection model of this work.…”

A Model-Based Strategy for Developing Sustainable Cosmetics Small and Medium Industries with System Dynamics

“…The open innovation dynamic strategies benefit companies because they collaborate between internal and external knowledge resources [30]. Optimal knowledge collaboration performance depends on selecting suitable and competent collaboration partners, considering the criteria for the similarity of knowledge domains between the company and partners, the level of communication, historical cooperation, and the benefits (revenues) obtained by both parties [31]. With the limitations of SMIs in skills, knowledge, technology, and capital, SMIs can combine open innovation communities in an open innovation dynamics strategy.…”

Leveraging Innovation Resources for Modular Products under Open Innovation Scenarios Using Fuzzy Distance Method

“…Shaikh pointed out that the selection of open innovation resources is even more complicated [15], and the evaluation of open innovation resources should pay more attention to value creation. In addition, knowledge matching and complementarity between external resources and an enterprise also play an important role in improving the latter's product innovation performance [16].…”